With the establishment of cloud services and applications, the data these applications produce can be widely dispersed. For example, a company may have all of its sales operations maintained by one cloud service (for example, currently at salesforce dot com), while all if its accounting data is maintained in another cloud service (for example, currently at workforce dot come). In both cases the information pertaining to a business customer must be maintained. The complexity of maintaining such information increases even further for large organizations that have significant data located on site because that on-site data also needs to be integrated with various cloud applications.
Such data distribution requires integration between the various applications (referred to as “end points”). The integration is implemented as integration flow and should be seen as simple data synchronization or orchestration of complex logic that manipulates and transforms the data. Typically, an Enterprise Service Bus (ESB) type of software conducts the implementation on site.
If the data lives in the cloud it is natural for the integration to happen in the cloud as well. These cloud integration services are referred to as “Integration Platform as a Service” (iPaaS) and may be defined as a suite of cloud services enabling development, execution and governance of integration flows connecting any combination of on-premises and cloud-based processes, services, applications and data within individual or across multiple organizations. A discussion of iPaaS is provided at gartner dot com.
An important issue with integration in general is that it manipulates systems of record in an automated way. It modifies large quantities of business critical data. Therefore proper testing and validation procedures are essential.
Conventional cloud based software itself and the iPaaS can be introduced/developed very fast in an organization. The software has zero footprint, usually is very fast to purchase and the end users can quickly start creating integrations. That instant behavior is transferred to the lifecycle of the created integrations. One can create, change and execute the integrations easily.
The problem with the instant change and execution is that an error could unwittingly change a large dataset. In some cases the error could be even unnoticed.